has_no_nulls

has_no_nulls(data, columns=None)

Check if a DataFrame has any null (missing) values.

Parameters

data: PolarsLazyOrDataFrame

The input DataFrame to check for null values.

columns: Optional[PolarsColumnType] = None

Columns to consider for null value check. By default, all columns are checked.

Returns

Name	Type	Description
	PolarsLazyOrDataFrame	The original polars DataFrame or LazyFrame when the check passes

Examples

>>> import polars as pl
>>> import pelage as plg
>>> df = pl.DataFrame({
...     "A": [1, 2],
...     "B": [None, 5]
... })
>>> df
shape: (2, 2)
┌─────┬──────┐
│ A   ┆ B    │
│ --- ┆ ---  │
│ i64 ┆ i64  │
╞═════╪══════╡
│ 1   ┆ null │
│ 2   ┆ 5    │
└─────┴──────┘
>>> df.pipe(plg.has_no_nulls)
Traceback (most recent call last):
    ...
pelage.types.PolarsAssertError: Details
shape: (1, 2)
┌────────┬────────────┐
│ column ┆ null_count │
│ ---    ┆ ---        │
│ str    ┆ u32        │
╞════════╪════════════╡
│ B      ┆ 1          │
└────────┴────────────┘
Error with the DataFrame passed to the check function:
--> There were unexpected nulls in the columns above